landsat_based_spectral_indices

Landsat-based Spectral Indices Data Cube

To support soil health monitoring, this data cube offers a time-series of Landsat-based spectral indices maps across continental Europe—including Ukraine, the UK, and Turkey—from 2000 to 2022. At a resolution of 30 meters, it includes bi-monthly, annual, and long-term analyses, focusing on key aspects of soil health such as vegetation cover, soil exposure, tillage practices, and crop intensity. Apart from direct monitoring, analysis, and verification of specific aspects of soil health, this data cube also provides important input for modeling and mapping soil properties. All the maps are aligned with the standard spatial/temporal resolution and sizes indicated/recomended by AI4SoilHealth project, Work Package - 5.

All the data within this folder is licensed under CC-BY-SA, and the code is licensed under the MIT License.

Please cite as:

• Tian, X., Consoli, D., Hengl, T., Schneider, F., Parente, L., Şahin, M., Minařík, R., Ho, Y., (2024?) "Time-series of Landsat-based spectral indices for continental Europe for 2000–2022 to support soil health monitoring", submitted to [PeerJ], preprint available at: [to be filled].

Summary

This folder provides

1. Essential code & data used to generate/analyze/visualize/upload the landsat-based spectral indices data cube,
2. Visualization for selected indices.

The indices include:

• Vegetation index: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR).
• Soil exposure: Bare Soil Fraction (BSF).
• Tillage and soil sealing: Normalized Difference Tillage Index (NDTI) and minimum Normalized Difference Tillage Index (minNDTI).
• Crop patterns: Number of Seasons (NOS) and Crop Duration Ratio (CDR).
• Water dynamics: Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI)

General steps of maps production are:

A preview of the BSF (%) time series for Europe from 2000 to 2022:

Access to the data cube

Yearly Landsat ARD Red band

• URL: https://stac.ecodatacube.eu/red_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: Red band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD red band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Landsat ARD Green band

• URL: https://stac.ecodatacube.eu/green_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: Green band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD green band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Landsat ARD Blue band

• URL: https://stac.ecodatacube.eu/blue_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: Blue band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD blue band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Landsat ARD Near-Infrared band (NIR)

• URL: https://stac.ecodatacube.eu/nir_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: NIR band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD NIR band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Landsat ARD Shortwave Near-Infrared band (SWIR1)

• URL: https://stac.ecodatacube.eu/swir1_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: SWIR1 band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD SWIR1 band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Landsat ARD Shortwave Near-Infrared 2 band (SWIR2)

• URL: https://stac.ecodatacube.eu/swir2_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: SWIR2 band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD SWIR2 band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Landsat ARD Thermal band

• URL: https://stac.ecodatacube.eu/thermal_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: Thermal band aggregated yearly from 30-m bi-monthly gapfilled GLAD Landsat ARD thermal band from 2000 to 2022.
• Theme: Surface reflectance
• DOI: https://doi.org/10.5281/zenodo.10851081

Bi-monthly Normalized Difference Vegetation Index (NDVI)

• URL: https://stac.ecodatacube.eu/ndvi_glad.landsat.ard2.seasconv/collection.json
• Description: [NDVI](Tucker, 1979) quantifies vegetation greenness, computed from 30-m bi-monthly aggregated and gapfilled GLAD Landsat ARD from 2000 to 2022.
• Theme: Vegetation
• DOI: https://doi.org/10.5281/zenodo.10884235

Bi-monthly Soil Adjusted Vegetation Index(SAVI)

• URL: https://stac.ecodatacube.eu/savi_glad.landsat.ard2.seasconv.bimonthly.m/collection.json
• Description: [SAVI](Huete, 1988) is a vegetation index that attempts to minimize soil brightness influences using a soil-brightness correction factor, computed from 30-m bi-monthly aggregated and gapfilled GLAD Landsat ARD from 2000 to 2022.
• Theme: Vegetation
• DOI: https://doi.org/10.5281/zenodo.10884235

Bi-monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

• URL: https://stac.ecodatacube.eu/fapar_glad.landsat.ard2.seasconv/collection.json
• Description: [FAPAR](Robinson et al., 2018) quantifies the fraction of the solar radiation absorbed by live leaves for the photosynthesis activity, computed from 30-m bi-montlhy NDVI from 2000 to 2022.
• Theme: Vegetation
• DOI: https://doi.org/10.5281/zenodo.10884235

Bi-monthly Normalized Difference Snow Index (NDSI)

• URL: https://stac.ecodatacube.eu/ndsi_glad.landsat.ard2.seasconv/collection.json
• Description: NDSI computed for 30-m bi-monthly aggregated and gapfilled GLAD Landsat ARD from 2000 to 2022.
• Theme: Vegetation
• DOI: https://doi.org/10.5281/zenodo.10884235

Bi-monthly Normalized Difference Water Index (NDWI, Gao)

• URL: https://stac.ecodatacube.eu/ndwi.gao_glad.landsat.ard2.seasconv.bimonthly.m/collection.json
• Description: [NDWI](Gao, 1996) indicates vegetation liquid water content, computed from 30-m bi-monthly aggregated and gapfilled GLAD Landsat ARD from 2000 to 2022.
• Theme: Water
• DOI: https://doi.org/10.5281/zenodo.10884235

Bi-monthly Landsat Normalized Difference Tillage Intensity (NDTI)

• URL: https://stac.ecodatacube.eu/ndti_glad.landsat.ard2.seasconv/collection.json
• Description: [NDTI](Van Deventer et al., 1997) differentiates crop residues from soil, computed from 30-m bi-monthly aggregated and gapfilled GLAD Landsat ARD from 2000 to 2022.
• Theme: Tillage
• DOI: https://doi.org/10.5281/zenodo.10884235

Yearly Normalized Difference Vegetation Index (NDVI)

• URL: https://stac.ecodatacube.eu/ndvi_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: NDVI aggregated yearly from bi-monthly NDVI time series from 2000 to 2022.
• Theme: Vegetation
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly Normalized Difference Water Index (NDWI, Gao)

• URL: https://stac.ecodatacube.eu/ndwi.gao_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: NDWI (Gao) aggregated yearly from bi-monthly NDWI (Gao)
• Theme: Water
• DOI: https://doi.org/10.5281/zenodo.10851081

Yearly minimum Normalized Difference Tillage Intensity (minNDTI)

• URL: https://stac.ecodatacube.eu/ndti.min_glad.landsat.ard2.seasconv.bimonthly.min/collection.json
• Description: Yearly minimum NDTI selected from bi-monthly NDTI at 30-m from 2000 to 2022.
• Theme: Tillage
• DOI: https://doi.org/10.5281/zenodo.10777869

Yearly Bare Soil Fraction (BSF)

• URL: https://stac.ecodatacube.eu/bsf_glad.landsat.ard2.seasconv.m.yearly/collection.json
• Description: BSF (bare soil fraction) computed for 30-m bi-monthly aggregated and gapfilled GLAD Landsat ARD from 2000 to 2022, to indicate the yearly duration a location stays bare.
• Theme: Soil exposure
• DOI: https://doi.org/10.5281/zenodo.10777869

Yearly Number of Seasons (NOS)

• URL: https://stac.ecodatacube.eu/nos_glad.landsat.ard2.seasconv/collection.json
• Description: Number of Seasons (NOS) derived from bimonthly NDVI time series at 30-m from 2000 to 2022, indicating the annual crop cycle numbers.
• Theme: Crop intensity
• DOI: https://doi.org/10.5281/zenodo.10777869

Yearly Crop Duration Ratio (CDR)

• URL: https://stac.ecodatacube.eu/cdr_glad.landsat.seasconv/collection.json
• Description: Crop Duration Ratio (CDR) measures the active cropping period's proportion of the year, calculated from bimonthly NDVI time series at 30-m from 2000 to 2022.
• Theme: Crop intensity
• DOI: https://doi.org/10.5281/zenodo.10777869

Long term trend of NDVI-P50 between 2000 and 2022

• URL: https://stac.ecodatacube.eu/ndvi_glad.landsat.ard2.seasconv.yearly.m.theilslopes/collection.json
• Description: NDVI slopes fitted on the annual NDVI P50 time series from 2000 to 2022.
• Theme: Vegetation
• DOI: https://zenodo.org/records/10776892

Long term trend of NDWI-P50 (Gao) between 2000 and 2022

• URL: https://stac.ecodatacube.eu/ndwi_glad.landsat.ard2.seasconv.yearly.m.theilslopes/collection.json
• Description: Slope fitted with Theil-Sen estimator on annual NDWI (Gao) time series between 2000 and 2022
• Theme: Water
• DOI: https://zenodo.org/records/10776892

Long term trend of BSF between 2000 and 2022

• URL: https://stac.ecodatacube.eu/bsf_glad.landsat.ard2.seasconv.yearly.m.theilslopes/collection.json
• Description: Slope fiited with Theil-Sen estimator on annual BSF time series between 2000 and 2022.
• Theme: Soil exposure
• DOI: https://zenodo.org/records/10776892

Long term trend of minNDTI between 2000 and 2022

• URL: https://stac.ecodatacube.eu/ndti.min.slopes_glad.landsat.ard2.seasconv.yearly.min.theilslopes/collection.json
• Description: Slope fiited with Theil-Sen estimator on annual minNDTI time series between 2000 and 2022.
• Theme: Tillage
• DOI: https://zenodo.org/records/10776892

Disclaimer

The production of this data cube is part of AI4SoilHealth project. The AI4SoilHealth project project has received funding from the European Union's Horizon Europe research an innovation programme under grant agreement No. 101086179. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided “as is”. AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.

Contacts

These maps are created by Xuemeng, Davide, Leandro, and Yu-Feng from OpenGeoHub. If you spot any problems in the maps, or see any possible improvements in them, or see any potential collaborations, or etc..., just raise an issue here or send us emails! We appreciate any feedbacks/helps that could refine them.